JP3953994B2 - Image processing information association processing apparatus, printing system, layout data output enabling method, and program - Google Patents

Abstract

Image features of a plurality of RGB images arranged for layout are extracted, and the RGB images are classified into image categories determined by the commonality of the image features. Recipe information previously set in accordance with the image features is added to layout data while being associated with RGB images for each image category. This enables an output device to perform an optimization process by reference to the added recipe information, thereby outputting good-quality printed matter excellent in color reproduction and the like if information for the process of optimizing RGB image data is not previously provided during the generation of the layout data.

Description

  The present invention relates to a conversion processing technique for optimizing RGB image data for printing in a printing system for commercial printing.

  With the widespread use of digital cameras, layout data to be printed originals is created using photographic images that are taken with digital cameras and are represented in the RGB color system (RGB images). / So-called RGB submissions are increasing (delivering) to the printing process. The RGB submitted layout data includes RGB image data that realizes an RGB image in accordance with a predetermined format (described), but this RGB image data can be printed in the plate making / printing process. Therefore, it is necessary to perform color space conversion processing for converting the image (CMYK image) represented by the CNYK color system into CMYK image data.

  In addition, chart images such as graphs created by spreadsheet software or presentation software, and so-called CG (computer graphics) images created by drawing software may also be created as RGB images. The number of RGB submissions using print originals laid out as images is increasing.

  The layout data is created as data in PDF (Portable Document Format) format, for example. When the layout data is described in the PDF format, the color space conversion process is performed collectively for all the images laid out based on the ICC profile embedded as header information in the layout data.

  However, the ICC profile is originally used for maintaining color reproducibility between input / output devices having different color reproduction characteristics, and color space conversion processing based on the ICC profile is incorporated in layout data. It is performed uniformly for all of the image data. Therefore, the picture making element (gamut Mapping) that has been conventionally performed by the operator of the scanner based on the rule of thumb is not reflected in the conversion process, and the sharpness process is not performed. On the other hand, it is common for a plurality of images to be laid out in a printed matter. However, if the type of each image (photograph, chart, CG, etc.) and the contents of expression are different, this is performed in order to enhance the expressive power. Since the contents of the power correction processing differ depending on the individual images, in order to obtain a high-quality printed matter, such image correction processing is individually performed on the image data realizing the individual images in the plate making / printing process. Need arises.

  Therefore, image processing to be performed on each RGB image in the plate making / printing process in the layout data including the photograph data for realizing the RGB image described in the PDF format at the time of the layout work in the production design process. Recipe data indicating the contents of color space conversion processing and image correction processing is incorporated (described), and the plate making / printing process interprets the contents of the recipe data and performs necessary processing, and then performs processing such as rasterization processing. A printing system (RGB image quality management system) is implemented (for example, see Non-Patent Document 1).

Dainippon Screen Mfg. Co., Ltd., “Digital Camera Image Optimization Software ColorGeniusDC”, [online], [searched August 22, 2003], Internet <URL: http://www.screen.co.jp/ga_product /colorgenius_dc/colorgenius_dc.html>

  When only the image conversion processing based on the ICC profile is performed on the submitted RGB layout data, one of the RGB image data in which the above image correction processing is incorporated in the layout data in the plate making / printing process. It needs to be done for each one. However, in recent plate making / printing processes, as represented by the intelligence workflow RIP system, automation of output processing is progressing, and work in the plate making / printing process mainly involves setting parameters relating to output. Such correction processing is incompatible with output work that has been automated.

  In addition to the conversion processing from RGB image data to CMYK image data, the image correction processing to be performed on a printing document so as to obtain high-quality printing is conventionally a completely separate process from the plate making / printing process. Therefore, there is also a problem that an operator having skill in such processing does not sufficiently exist in the plate making / printing process.

  When the above-described RGB image quality management system is used, the above-mentioned problems in the plate making / printing process are solved, but in such a system, the side of the production design process, which is the sender of layout data, prior to the layout of the printed matter. It is necessary to have special software that can create recipe data. In the production design process, layout data that has undergone such a special process is not necessarily created.

  The present invention has been made in view of the above problems, and image processing information for optimizing the RGB image data for printing when the layout data created as a printing document includes RGB image data. An object of the present invention is to provide an image processing information associating apparatus for associating the image data with the RGB image data in the layout data and a printing system including the image processing information associating apparatus.

In order to solve the above problems, the invention of claim 1 is characterized in that when an output process based on layout data described in a page description language and including at least one RGB image data is performed in a predetermined output device, the output process is performed. An apparatus for performing processing for associating image processing information necessary for image processing to be performed with respect to each of the at least one RGB image data before the image processing to be performed with respect to each of the RGB image data. By searching an image processing information database in which a plurality of pieces of image processing information having different processing contents are registered, layout data reading means for reading the layout data, and description contents regarding the RGB image data of the layout data , wherein at least one of the RGB image data Rey Image data extraction means for extracting from image data, image feature quantity calculation means for calculating at least one image feature quantity for each of the extracted at least one RGB image data, and the at least one image feature quantity and a predetermined classification Image classification means for collating the reference and classifying the at least one RGB image data into a predetermined image category according to the classification criterion, each of the at least one RGB image data, and the image processing Association processing means for associating with the designated one of the plurality of image processing information registered in the information database; and composition means for synthesizing the image processing information to be associated with the layout data. It is characterized by providing.

  A second aspect of the present invention is the image processing information association processing apparatus according to the first aspect, wherein the image processing includes color space conversion processing and image correction processing.

  A third aspect of the present invention is the image processing information associating apparatus according to the first or second aspect, wherein the associating means is classified into the same image category of the at least one RGB image data. The same image processing information can be simultaneously associated with existing RGB image data.

  According to a fourth aspect of the present invention, there is provided the image processing information associating apparatus according to any one of the first to third aspects, wherein the RGB image is output from the predetermined output device based on the associated image processing information. The image processing performed on the data further includes simulation processing means capable of simulating.

  According to a fifth aspect of the present invention, there is provided the image processing information association processing apparatus according to the fourth aspect, wherein fine adjustment is performed to finely adjust the associated image processing information in response to an instruction input based on the simulation result. Means.

  A sixth aspect of the present invention is the image processing information associating apparatus according to any one of the first to fifth aspects, wherein the image feature amount calculating unit is configured to display each color of the RGB image represented by the RGB image data. Steep peak counting means for counting the number of steep peaks, which is an image feature amount indicating the number of peaks that are sharper than a predetermined level appearing in the gradation value histogram of the component, is provided.

  A seventh aspect of the present invention is the image processing information associating apparatus according to any one of the first to fifth aspects, wherein the image feature amount calculating unit is configured to display each color of the RGB image represented by the RGB image data. And an edge component amount calculating means for calculating an edge component amount that is an image feature amount indicating an edge presence ratio of the component with respect to the entire image.

  The invention according to claim 8 is the image processing information association processing apparatus according to any one of claims 1 to 5, wherein the image feature amount calculation means is configured to perform predetermined processing on the RGB image represented by the RGB image data. High saturation pixel ratio calculation means for calculating a high saturation pixel ratio, which is an image feature amount indicating a ratio of pixels having a saturation higher than the threshold value.

  A ninth aspect of the present invention is the image processing information association processing apparatus according to any one of the first to fifth aspects, wherein the image feature amount calculating means is an RGB image represented by the RGB image data. And a specific pixel ratio calculating means for calculating a specific color pixel ratio which is an image feature amount indicating a ratio of pixels belonging to a specific range with a gradation value.

  A tenth aspect of the present invention is the image processing information association processing apparatus according to any one of the first to fifth aspects, wherein the image feature amount calculating means is expressed in an RGB image represented by the RGB image data. Image line segment amount calculating means for calculating an image line segment amount that is an image feature amount indicating the amount of the line segment being displayed.

The invention according to claim 11 is a) an output device, which is described in a page description language , and when performing output processing based on layout data including at least one RGB image data, prior to the output processing, the at least An output device comprising image processing means for performing image processing to obtain image data that can be output for each of RGB image data, and b) an image processing information associating device, b-1 ) Image processing information database which is image processing information which is information that enables the image processing to be performed in the output device and in which image processing information having different processing contents is registered; and b-2) at least one RGB image data Layout data reading means for reading second layout data including: b-3) a description relating to the RGB image data of the layout data Image data extraction means for extracting the at least one RGB image data by retrieving the description content, and image feature amount calculation for calculating at least one image feature amount for each of the extracted at least one RGB image data And b-4) an image for collating the at least one image feature quantity with a predetermined classification criterion and classifying the at least one RGB image data into a predetermined image category according to the classification criterion according to the result. A classifying means; and b-5) an association processing means for associating each of the at least one RGB image data with a designated one of the plurality of image processing information registered in the image processing information database; , B-6) Combining the image processing information to be associated with the second layout data That the synthesizing means, and the image processing information association device comprising, characterized in that it comprises a.

The invention of claim 12 is a method for enabling output processing based on layout data described in a page description language and including at least one RGB image data in a predetermined output device , wherein An image data extracting step for extracting at least one RGB image data; an image feature amount calculating step for calculating at least one image feature amount for each of the extracted at least one RGB image data; and the at least one image feature. Each of the at least one RGB image data; and an image classification step of collating the quantity with a predetermined classification criterion and classifying the at least one RGB image data into a predetermined image category according to the classification criterion according to the result Registered in the predetermined image processing information database An association processing step for associating with the designated one of the pieces of information for performing image processing, wherein the RGB image data is image data that can be output. A synthesis step for synthesizing the target image processing information with the layout data; and a storage step for storing the synthesized layout data so as to be sent to the output device. .

  According to a thirteenth aspect of the present invention, in the output processing enabling method according to the twelfth aspect, the image processing includes color space conversion processing and image correction processing.

  The invention of claim 14 is the output processing enabling method according to claim 12 or claim 13, wherein the associating step is classified into the same image category of the at least one RGB image data. The same image processing information can be associated with RGB image data at the same time.

  A fifteenth aspect of the invention is the output processing enabling method according to any one of the twelfth to fourteenth aspects, wherein the RGB image data is output from the predetermined output device based on the associated image processing information. The image processing performed on the computer is further provided with a simulation processing step capable of simulating.

  The invention according to claim 16 is the output processing enabling method according to claim 15, wherein the fine adjustment step of finely adjusting the associated image processing information in response to an instruction input based on the result of the simulation. Are further provided.

  The invention according to claim 17 is the output processing enabling method according to any one of claims 12 to 16, wherein the image feature amount calculating step is performed for each color component of the RGB image represented by the RGB image data. A steep peak counting step of counting the number of steep peaks, which is an image feature amount indicating the number of peaks that are sharper than a predetermined level appearing in the gradation value histogram.

  The invention according to claim 18 is the output processing enabling method according to any one of claims 12 to 16, wherein the image feature amount calculation means calculates the color components of the RGB image represented by the RGB image data. And an edge component amount calculating means for calculating an edge component amount that is an image feature amount indicating an edge presence ratio with respect to the entire image.

  A nineteenth aspect of the invention is the output processing enabling method according to any one of the twelfth to sixteenth aspects, in which the image feature amount calculating means performs a predetermined operation on an RGB image represented by the RGB image data. High saturation pixel ratio calculating means for calculating a high saturation pixel ratio, which is an image feature amount indicating a ratio of pixels with higher saturation than the threshold value.

  The invention according to claim 20 is the output processing enabling method according to any one of claims 12 to 16, wherein the image feature amount calculating means is an RGB image represented by the RGB image data. And a specific pixel ratio calculating means for calculating a specific color pixel ratio that is an image feature amount indicating a ratio of pixels belonging to a specific range with gradation values.

  The invention according to claim 21 is the output processing enabling method according to any one of claims 12 to 16, wherein the image feature amount calculation means is expressed in an RGB image represented by the RGB image data. Image line segment amount calculating means for calculating an image line segment amount which is an image feature amount indicating the number of existing line segments.

According to a twenty-second aspect of the present invention, by using a CPU and a memory included in a computer, the computer is caused to function as the image processing information association processing device according to any one of the first to tenth aspects.

  According to the first to twenty-second aspects of the present invention, even when information for performing optimization processing on the RGB image data is not given in advance at the time of creating the layout data, the output device converts the layout data into the layout data. Referring to the synthesized image processing information, RGB image data can be processed to be image data that can be output, and high-quality printed matter with excellent color reproducibility is output. can do.

  In particular, according to the inventions of claim 3 and claim 14, a plurality of laid out RGB images are classified according to the commonality of features represented by the RGB images, and the RGB images belonging to the same image category are classified. Since the same image processing information can be applied simultaneously, even when many images are laid out, the complexity of the association processing is suppressed.

  In particular, according to the inventions of claims 4, 5, 15, and 16, the validity of the association of the image processing information can be confirmed by simulation. It is possible to reliably output a high-quality printed matter.

  In particular, according to the inventions of claims 5 and 16, since the image processing information once applied can be finely adjusted, the image processing information most suitable for the expression content of each image can be associated with the output. In the apparatus, it is possible to reliably output a high-quality printed matter excellent in color reproducibility of individual images.

  In particular, according to the inventions of claims 6 and 17, it is easy to classify artificially created images such as CG images and color-coded business graphs.

  In particular, according to the inventions of claims 7 and 18, it is easy to determine whether or not sharpness correction is necessary again.

  In particular, according to the inventions of claims 8 and 19, it becomes easy to classify artificially created images such as CG images and color-coded business graphs.

  In particular, according to the ninth and twentieth inventions, classification of images represented by specific color components such as a person and a landscape is facilitated.

  In particular, according to the tenth and twenty-first aspects of the present invention, it is easy to classify an image representing an artifact and an image representing a natural object.

<Overall system configuration>
FIG. 1 is a diagram showing a configuration of a printing system 1 according to an embodiment of the present invention. The printing system 1 mainly includes a layout data creation device 2, an image processing information association processing device 3, and an output device 7. The layout data creation device 2 is a device for creating layout data DL (FIG. 4) to be a printed document (printing document) to be created using a photographic image, a chart image, a CG image, or the like. is there. The image processing information association processing device 3 uses recipe information (details will be described later), which is image processing information related to processing (optimization processing) for optimizing image data included in the layout data for printing, as RGB image data. It is an apparatus for adding (incorporating) to layout data while associating. Here, the optimization process refers to a color space conversion process and an image correction process, which are performed for the purpose of obtaining a high-quality printed matter with respect to layout data including RGB image data in printing based on the layout data. The output device 7 interprets the description contents of the layout data (layout data with recipe) to which predetermined recipe information is added, performs the optimization process described in the recipe information, and then performs rasterization and final output The processing is performed. Note that data exchange between the layout data creation device 2 and the image processing information association processing device 3 and between the image processing information association processing device 3 and the output device 7 constitutes a network of these devices. It may be a mode performed via the communication lines CB1 and CB2, or a mode performed via a portable recording medium.

  The layout data creation device 2 can acquire photographic data obtained by photographing with the digital camera 5 and use it for the layout of a document for printing. Further, the image data acquired by the input device such as the image scanner 6 may be used for the layout. The image data is transferred from the digital camera 5 or the image scanner 6 to the layout data creating apparatus 2 by recording the image data on connection cables CB3 and CB4 such as a USB cable or on a portable recording medium (not shown). This is realized by reading this in the layout data creation device 2.

  The layout data creation device 2 is realized by a general-purpose personal computer. In the layout data creation device 2, layout software 21, spreadsheet software 22, presentation software 23, draw software 24, and the like are installed. Images are appropriately created and processed by these software, and then the image is created. Layout data to be a printing document including the image data, that is, layout data DL in which image data is incorporated is created. As each software, what is already marketed can be used. Preferably, the layout data DL is described in units of pages in a page description language (PDL). More preferably, it is described in the PDF format.

  The image processing information association processing device 3 is also realized by a general-purpose personal computer. The image processing information association processing device 3 includes a CPU 31a, a ROM 31b, and a RAM 31c. The image processing information association processing device 3 includes a control unit 31 that implements each function to be described later, a hard disk, and the like. A storage unit 32 for storing a predetermined operation program 32p (FIG. 3) for realizing the processing information association processing apparatus 3, an operation unit 33 such as a mouse and a keyboard for an operator to input various instructions, a display, etc. Display unit 34, R / W unit 35 for reading / writing data to / from various portable recording media, and an interface for transferring data to / from other devices A communication unit 36 is mainly provided.

  In the image processing information association processing apparatus 3, a so-called GUI (Graphical User Interface) capable of performing processing while displaying the operation content through the operation unit 33 and the processing status of various processes on the display unit 34. Is realized by the functions of the control unit 31, the operation unit 33, and the display unit 34. Processing in each unit described later realized in the control unit 31 is also performed using this GUI. FIG. 2 is a diagram schematically showing a work window W displayed on the display unit 34. However, an instruction content or work situation instructed through the operation unit 33 by the action of the GUI is displayed in such a work window W. A desired process can be performed while displaying.

  FIG. 3 is a diagram for explaining functions realized in the control unit 31 of the image processing information association processing apparatus 3. FIG. 4 is a diagram for explaining the data flow in the additional processing.

  In the control unit 31, by executing a predetermined operation program 32 p stored in the storage unit 32, the data input / output unit 40, the image processing unit 50, the recipe are performed by the functions of the CPU 31 a, the ROM 31 b, and the RAM 31 c. The information processing unit 60 is realized.

  The data input / output unit 40 is created through a process of reading one layout data DL selected by an operator from one or a plurality of layout data DL stored in the storage unit 32 and a predetermined process described later. It is responsible for storing the recipe-added layout data DLR in the storage unit 32 so that it can be sent to the output device 7.

  The image processing unit 50 mainly performs processing for extracting and classifying the image data DP incorporated in the layout data DL to be processed. For this purpose, the image processing unit 50 mainly includes an image extraction processing unit 51, an image feature amount calculation unit 52, and an image classification processing unit 53.

  The image extraction processing unit 51 performs processing (image data extraction processing) of analyzing the description content of the read layout data DL and extracting image data DP representing an RGB image. In the layout data DL, for example, contents such as image data and document data, layout information, and the like are described in a predetermined page description language such as a PDF format. Therefore, the image extraction processing unit 51 stores the image data in the layout data DL. Search the part to be expressed and extract its contents. A CMYK image represented in advance in the CMYK color system may be extracted.

  Based on the description content of the extracted image data DP, the image feature amount calculation unit 52 calculates an image feature amount for specifying the image feature of the RGB image PC2 represented by the image data DP (image feature). Responsible for quantity calculation processing). Here, as will be described later, the image feature amount is a general term for the amount acquired (counted or calculated) to be used when determining and classifying what kind of features the RGB image PC2 has. It is. As described below, the image feature amount calculation unit 52 acquires a predetermined image feature amount, each of which includes a steep peak counting unit 52a, an edge component amount calculation unit 42b, a high saturation pixel ratio calculation unit 42c, and a specific A color pixel ratio calculation unit 52d and an image line segment amount calculation unit 52e are provided.

  The steep peak counting unit 52a performs processing (steep peak counting processing) for counting the number of steep peaks (the number of steep peaks) more than a predetermined degree in the RGB image represented by the image data DP. The number of steep peaks is an image feature amount representing the number of steep peaks appearing in a histogram when the frequency distribution of gradation values in each color component of the RGB image is a histogram. A large number of steep peaks means that the image is an image having an image feature in which only a specific gradation value color protrudes and there are many. In this case, it means that there is a high possibility that the target image is an artificially created image such as a CG image or a color-coded business graph.

  The edge component amount calculation unit 52b performs processing for calculating the edge component amount of each color component of the RGB image represented by the image data DP (edge component amount calculation processing). The edge component amount is an image feature amount indicating a presence ratio of an edge (a sharp change point of the gradation value) with respect to the entire image. A large amount of edge component means that the image has an image feature that it is a sharp image with clear boundaries between regions with different color components, and is an image that has been sharpness corrected once. This means that the image is highly likely not to need the sharpness correction again.

  The high saturation pixel ratio calculation unit 52c is responsible for processing for calculating a high saturation pixel ratio for the RGB image represented by the image data DP (high saturation pixel ratio calculation processing). The high saturation pixel ratio is an image feature amount indicating the ratio of pixels having high saturation in the RGB image. In other words, it is the area ratio of the area occupied by pixels with high saturation out of the total area occupied by the image. The fact that there are many areas occupied by pixels with high saturation in an image means that the image is an image with a clear contrast and is mainly composed of conspicuous color components. This means that there is a high possibility that the image is an artificially created image such as a created business graph.

  The specific color pixel ratio calculation unit 52d performs processing (specific pixel ratio calculation processing) for calculating a specific color pixel ratio for the RGB image represented by the image data DP. The specific color pixel ratio is an image feature amount indicating a ratio of pixels belonging to a specific range with a gradation value in an RGB image. In other words, it is the area ratio of the area occupied by pixels in a certain gradation value (color density) range out of the total area occupied by the image. For example, it can be inferred that when there are many skin color components, the image is mainly representing a person, and when there are many blue and green components, it is an image mainly representing a landscape.

  The image line segment calculation unit 52e is responsible for processing for calculating the image line segment amount in the RGB image represented by the image data DP (image line segment calculation process). The image line segment amount is an image feature amount indicating the number of line segments expressed in the RGB image. For example, when many lines are drawn, it is presumed that there is a high possibility that the image represents an artificial object such as a machine, not a natural object such as a landscape.

  By calculating the various image feature amounts as described above in each part of the image feature amount calculation unit 52, the image feature amount data DPC which is a data set including these components is obtained. The method for calculating each image feature amount will be described in detail later.

  The image classification processing unit 53 performs processing (image classification processing) for classifying the RGB image represented by the image data DP incorporated in the layout data DL into several predetermined image categories according to the image characteristics. . The image classification process is performed by comparing the image feature data DPC obtained by the image feature data calculator 52 with classification reference data DCS determined in advance for each image category. As a result of such processing, image classification data DC is generated. As a result, the plurality of RGB images laid out are classified according to the commonality of the features represented by the RGB images.

  The image classification data DC is, for example, “1” when the k-th image data DP included in the layout data DL corresponds to the x-th classification standard described in the classification standard data DCS. The flag can be described in a format such as (k, x, 1) or (k, x, 0), assuming that a flag of “0” is given if not applicable.

  For specific image categories, for example, whether or not an image with sufficient quality can be obtained by printing only with a color space conversion process from an RGB image to a CMYK image and a standard correction process (printability). In the case of categorization from the viewpoint of (printability categorization), the conformity category is a general image that has been captured with a digital camera or read with a scanner and no correction has been made ( Natural images, unprocessed images). On the other hand, in order to obtain a high-quality printed image, specific color components are selectively used for the non-conforming category that is determined to be specially corrected, and the gradation value is distributed discretely when the frequency distribution is taken. Such a CG image or business image (graph image, presentation image, etc.), an image that has already undergone sharpness processing, an image with a lot of noise, or a blurred image such as a photograph that is out of focus. In this case, the classification reference data DCS includes the following contents, for example.

・ If Np ≧ 4, non-conforming image;
・ If Lav ≧ 10, sharpness processing is not required (nonconforming image);
If Dc = 0, conforming image (general image), if not = 0, nonconforming image.

  In addition, when categorizing according to the contents (image expression contents) expressed by the image (image expression contents categorization), it can be classified into a person category, a landscape category, a machine category, and the like. In this case, the classification reference data DCS includes the following contents, for example.

・ If skin color pixel ratio ≧ 0.8, person category;
If the green pixel ratio ≧ 0.4 and the blue pixel ratio ≧ 0.4, the landscape category;
・ If the image line segment ≧ 0.7, it is the machine category.

  The image category is a unit for creating and applying the recipe information RI described below. That is, as a general rule, one recipe information RI is set for one image category.

  The recipe information processing unit 60 is responsible for processing related to the recipe information RI. For this purpose, the recipe information processing unit 60 mainly includes a recipe information creation unit 61, a recipe information application unit 62, a simulation processing unit 63, and a recipe information synthesis unit 64.

  The recipe information creation unit 61 is responsible for creating recipe information RI and storing it in a recipe information database (DB) 32 d provided in the storage unit 32. In addition, as shown in FIG. 2, the recipe information RI stored in the recipe information DB 32d is displayed together with the recipe icon IC in the recipe information list field F3 of the work window W.

  Here, the recipe information RI will be described. The recipe information RI is processing information that describes the contents of optimization processing for printing to be performed prior to output processing on the image data DP incorporated in the submitted layout data DL in the output device 7. is there. The recipe information RI is described in the same description format as the layout data DL. Therefore, if the layout data DL is described in the PDF format, the recipe information RI is also described in the PDF format. If the image category to which the image laid out on the print document belongs is the same, the color component to be emphasized in the image and the point regarded as important in the color reproduction are considered to be almost common, so the recipe information RI is preferably It is set for each image category and registered in advance in the recipe information DB 32d. FIG. 5 is a diagram schematically showing the configuration of recipe information RI. As shown in FIG. 5A, the recipe information RI includes a color space conversion reference table (LUT) RI1, a characteristic setting parameter RI2, and a fine adjustment parameter RI3.

  The color space conversion reference table RI1 is set in order to refer to the color space conversion process for maintaining the color reproducibility of the RGB image laid out on the print document among the optimization processes for printing in the printed matter. The For example, conversion from the original RGB color space to the color space represented by JapanColor2001 is set.

  The characteristic setting parameter RI2 is a setting parameter that determines specific processing contents of image correction processing to be performed on the image data DP in the printing optimization processing. FIG. 6 is a diagram showing a setting item table TBL1 which is a list of setting items in the characteristic setting parameter RI2. Specific setting values of each item of the characteristic setting parameter RI2 shown in FIG. 6 are appropriately set for each image category according to the image characteristics of the images belonging to the image category.

  The items of the characteristic setting parameter RI2 will be described in order. The gradation level setting P1 is an item for setting a halftone density range. The tone curve setting P2 is an item for setting a halftone or a halftone using a tone curve. The color correction setting P3 is an item for correcting a deviation from an ideal color characteristic of ink. The K version setting P4 is an item for setting UCR, GCR, and the like for replacing the overlapping portion of each color of CMY with the K version. The gray balance setting P5 is an item for setting the balance of each color of CMY for expressing proper gray. The sharpness setting P6 is an item for enhancing the sharpness of an image by USM (unsharp masking) or the like. The resolution setting P7 is an item for converting the resolution of an image by pixel interpolation processing or the like.

  FIG. 7 shows a correspondence relationship between an image category determined based on certain classification reference data DCS and the content of correction processing to be performed on an image belonging to the image category prior to printing as a correction content table TBL2. FIG. Each characteristic setting parameter RI2 of the recipe information RI is set for the images belonging to the respective image categories so as to realize the correction processing content shown in FIG. As shown in FIG. 2, when the recipe information RI listed in the recipe information list field F3 is selected in the work window W, the corresponding correction processing content in the correction content table TBL2 is displayed in the recipe information detail display. Displayed in column F4. FIG. 2 illustrates a case where “machine” recipe information is selected.

  When the color space conversion reference table RI1 and the characteristic setting parameter RI2 are appropriately set in the recipe information RI, the output device 7 performs an optimization process on the image data DP in accordance with the description contents of the recipe information RI and outputs it. Processing will be performed. As a result, even if the original for printing is submitted in RGB, it is possible to output a high-quality printed matter by performing such optimization processing.

  The fine adjustment parameter RI3 is a parameter that is set for the operator to make further fine adjustments for each item of the characteristic setting parameter RI2. As described above, since one recipe information RI is set for one image category in principle, the same optimization process is performed on the image data DP representing images belonging to the same image category. This is considered a reasonable process. However, even if the images belong to the same image category, there are some differences in the contents of the expression. Therefore, it is not always necessary for each image to perform the optimization process uniformly using the same recipe information RI. It is not necessarily “optimal” processing. In such a case, the fine adjustment parameter RI3 corresponds to a change amount for manually fine-tuning the set value of the characteristic parameter RI2 given as a default value so as to enable truly “optimal” processing. It is a parameter to do. For the actual fine adjustment, a mode in which typical adjustment contents are prepared in advance as a processing menu and set according to the menu is preferable. FIG. 8 is a diagram showing a fine adjustment menu table TBL3 that is a list of typical processing menus for setting the fine adjustment parameter RI3.

  Returning to the description of the recipe information processing unit 60, the recipe information application unit 62 is responsible for processing in which the operator associates the image data DP incorporated in the layout data DL and the recipe information RI (recipe information association processing). It also handles processing for correcting the recipe information RI once associated. Here, the recipe information association process is a process for associating the layout data DL with recipe information RI suitable for each image data DP incorporated in the layout data DL.

  The recipe information application unit 62 generates temporary data DT in which the layout data DL or the image data DP incorporated therein is associated with the applied recipe information RI. That is, generation of temporary data DT is an essential process of the recipe information association process. Similar to the operation realized in the recipe information composition unit 64 described later, the temporary data DT may be generated by directly adding the recipe information RI to the layout data DL, or the layout data DL or A mode in which association information is given to the recipe information RI to generate a data set composed of both may be used. The temporary data DT is used for image simulation processing by the simulation processing unit 63 described below. Note that by clicking the delete button BT2, all the associated recipe information is erased, and a new recipe information association process is performed again by the operation of the recipe information application unit 62.

  Based on the temporary data DT, the simulation processing unit 63 simulates on the work window W an optimization process for printing performed by the output device 7 on the image data DP incorporated in the layout data DL. Responsible for processing (simulation processing). Further, by clicking the print button BT3, for example, the proof printing based on the temporary data DT can be performed by the proofing device 4 such as a color ink jet printer connected by the connection cable CB5.

  The recipe information synthesizing unit 64 synthesizes and adds the confirmed recipe information RIF that is the recipe information RI at the time when the operator determines that the recipe information RI is appropriately set as a result of performing the above-described simulation processing and the like to the layout data DL. Responsible for processing (recipe information addition processing). Thereby, the layout data DLR with a recipe, which is the layout data DL to which the recipe information RI is added, is generated.

  The layout data DLR with recipe thus obtained is stored in the storage unit 32 so as to be able to be sent to the output device 7, and is appropriately transferred to the output device 7 to be subjected to predetermined processing. Become.

  The output device 7 is a device that performs predetermined processing on the layout data received from the image processing information association processing device 3 and outputs a printed matter. In the present embodiment, prior to such output processing, first, based on the description content of the confirmed recipe information RIF, optimization processing for printing, that is, for each image, the optimum color for each expression content The output process is performed after the space conversion process and the image correction process are performed. The output device 7 includes a control unit 71, a storage unit 72, an operation unit 73, a display unit 74, an R / W unit 75, a communication unit 76, and the like that are configured by a CPU 71a, a ROM 71b, and a RAM 71c. In addition to having each unit that realizes its function by a computer, it further includes a printing unit 77 that performs printing on a predetermined printing paper.

  FIG. 9 is a diagram for explaining functions realized in the control unit 71 of the output device 7. In the control unit 71, by executing a predetermined program 72p stored in the storage unit 72, the image conversion processing unit 81, the RIP processing unit 82, and the print control are performed by the operations of the CPU 71a, the ROM 71b, and the RAM 71c. The unit 83 is realized.

  The image conversion processing unit 81 applies the layout data DLR with recipe received from the image processing information association processing device 3 to the image data DP according to the contents of the recipe information RI added in the image processing information association processing device 3. Provided for performing optimization processing.

  For this purpose, the image conversion processing unit 81 mainly includes a layout data analysis unit 81a, a color space conversion unit 81b, a color tone correction unit 81c, a sharpness correction unit 81d, and a resolution conversion unit 81e.

  The layout data analysis unit 81a analyzes the description content of the layout data DLR with recipe, and extracts the recipe information RI incorporated in the layout data DLR and the image data DP to be optimized by the recipe information RI. To do.

  The color space conversion unit 81b performs CMYK conversion processing on the image data DP according to the color space conversion reference table RI1 described in the recipe information RI.

  The color tone correction unit 81c executes the correction of the gray level and the tone curve for the image data subjected to the CMYK conversion according to the description contents of the characteristic setting parameter RI2 and the fine adjustment parameter RI3 in the recipe information RI. The sharpness correction unit 81d performs USM correction according to the contents of the recipe information RI. The resolution conversion unit 81e converts the resolution of the image according to the content of the recipe information RI. The recipe-added layout data DLR (referred to as optimized layout data), which has been processed in each of these parts for all the incorporated image data DP, is once stored in the storage unit 72 and then stored in the RIP processing unit. 82.

  The RIP processing unit 82 is provided for performing rasterization processing (RIP processing) for converting the optimized layout data into raster data corresponding to each color plate of CMYK that can be processed by the printing unit 77. As the RIP processing technique, a known technique can be used.

  The print control unit 83 is provided for controlling execution of printing in the printing unit 77. Under the control of the print control unit 83, the printing unit 77 executes printing on a predetermined printing sheet based on the data for each color plate obtained by the RIP process.

  In the present embodiment, the output device 7 has been described in a mode in which both the RIP process and the print process are performed. However, these modes may be executed in separate devices.

<Flow of recipe information addition processing>
FIG. 10 is a diagram for explaining the flow of the process of adding recipe information RI to the layout data DL. The process of adding the recipe information RI starts from the process of reading the layout data DL to be processed by the operation of the data input / output unit 40 (step S1). When the layout data DL is read, the page layout image PL1 is displayed (reduced display) in the layout image display field F1 of the work window W as shown in FIG. Note that FIG. 2 is described in the PDF format, has a file name “leaflet.pdf”, and layout data DL that is a print document for all five pages is a processing target. The third page is a page. The case where it is displayed as the layout image PL1 is illustrated. It is assumed that four RGB images PC1 to PC4 are arranged on the third page.

  When the layout data DL is read, image data extraction processing is next performed by the action of the image extraction processing unit 51 (step S2). The image data DP corresponding to the RGB images PC1 to PC4 are extracted and temporarily stored in the RAM 31c, and the corresponding thumbnail images PS1 to PS4 are displayed in the recipe application column of the work window W in the case shown in FIG. A list is displayed in the image display row F21 of F2.

  When all the image data DP are extracted, an image feature amount calculation process is performed (step S3). As described above, as the image feature amount calculation processing, the steep peak counting processing by the operation of the steep peak counting portion 52a, the edge component amount calculation processing by the operation of the edge component amount calculating portion 52b, and the high saturation pixel ratio calculating portion 52c A high saturation pixel ratio calculation process by the action, a specific pixel ratio calculation process by the action of the specific color pixel ratio calculation unit 52d, and an image line segment calculation process by the action of the image line segment calculation unit 52e are performed.

  FIG. 11 is a diagram for explaining the flow of a steep peak counting process as a process for counting the number of peaks steep beyond a predetermined level. Hereinafter, the image data DP represents an image of m pixels × n pixels, and each pixel represents an 8-bit image in which each color component of R (Red), G (Green), and B (Blue) has 256 gradations. A case will be described as an example. In this case, in the image data DP, the gradation values at the pixel (i, j) (i and j are natural numbers) are (R (i, j), G (i, j), B (i, j)). (0 ≦ R (i, j), G (i, j), B (i, j) ≦ 255, R (i, j), G (i, j), B (i, j) integer).

  In the steep peak counting process, first, frequency distribution data is obtained by counting the number of pixels (frequency) taking each gradation value from 0 to 255 for each color component (step S21). The frequency corresponding to the color component c and the gradation value L (L = 0 to 255, L is an integer) is defined as frequency distribution data Hb (c, L). Note that c = 0, 1, and 2 correspond to flags representing R, G, and B, respectively.

Next, the obtained frequency distribution data is subjected to an averaging process for averaging the frequency with the previous and subsequent gradation values to obtain averaged distribution data (step S22). The averaging process is performed for each gradation value by averaging the frequencies in the gradation value range of the averaging range Rp centered on the gradation value. For example, when Rp = 3, the averaged distribution data Hbf (c, L) is
Hbf (c, L) = (Hb (c, L-1) + Hb (c, L) + Hb (c, L + 1)) / 3 (1)
(2 ≦ L ≦ 254).

  Once the averaged distribution data is obtained, the peak index calculation process is performed based on the average distribution data, and the peak index used for the peak determination process for determining whether each gradation value corresponds to the peak position in the frequency distribution histogram Data is calculated (step S23). Expression 2 is an expression for obtaining peak index value data Hbp (c, L).

Hbp (c, L) = (Hb (c, L) −Hbf (c, L)) / (Hbf (c, L) + mn / 4096.0) (Expression 2)
Peak determination processing is performed using the obtained peak index data (step S24). In the peak determination process, it is determined whether or not the peak index is larger than a peak threshold value Tp that is set in advance as a reference for whether or not the peak is steeper than a predetermined level. That is, for all c and L,
Hbp (c, L)> Tp (Equation 3)
It is determined whether or not the above is satisfied.

  Then, for each color component, the number of gradation values L satisfying Expression 3 is counted, and this is summed for all the color components, whereby the steep peak number Np for the image data DP is acquired. .

FIG. 12 is a diagram for explaining the flow of the edge component amount calculation processing. In the edge component amount calculation processing, first, calculation processing (Laplacian filter processing) is performed for applying the 3 × 3 Laplacian filter FT1 shown in FIG. 15 to the tone value of each pixel for each color component (step). S31). For example, the gradation value Rf (i, j) after the Laplacian filter processing of the R color component in the pixel (i, j) is
Rf (i, j) = 4 * R (i, j) -R (i-1, j) -R (i, j-1) -R (i + 1, j) -R (i, j + 1) )
.... (Formula 4)
Will be required. However, Rf (i, j) = − 256 if Rf (i, j) ≦ −256 as a result of calculation according to Equation 4, and Rf (i, j) = 255 if Rf (i, j) ≧ 255. Shall be replaced.

Next, the absolute values of the gradation values obtained by the Laplacian filter processing are averaged for all the pixels (step S32), and this is summed for each color component to obtain an edge amount average value. The average edge amount Lav is
Lav = {(Σ | Rf (i, j) |) + (Σ | Gf (i, j) |) + (Σ | Bf (i, j) |)} / mn
... (Formula 5)
Is required.

When the edge amount average value is obtained, the square root is calculated (step S33). This is defined as the amount of edge component. That is, the edge component amount E is
E = (Lav) 1/2 (Equation 6)
Is required.

  In addition, instead of summing up the edge average amount for each color component in Equation 5, a square root may be taken for each color component as shown in Equation 6 to obtain the edge component amount corresponding to each color component.

FIG. 13 is a diagram for explaining the flow of high saturation pixel ratio calculation processing. In the high saturation pixel ratio calculation process, first, the saturation of each pixel is calculated (step S41). The saturation C (i, j) at pixel (i, j) is
C (i, j) = {(R (i, j)) 2+ (G (i, j)) 2+ (B (i, j)) 2
-R (i, j) * G (i, j) -G (i, j) * B (i, j) -B (i, j) * R (i, j)}
.... (Formula 7)
Is required.

  Next, frequency distribution data is obtained by counting the number of pixels (frequency) taking each gradation value from 0 to 255 based on the obtained saturation of each pixel (step S42). The frequency corresponding to the gradation value L (L = 0 to 255, L is an integer) is defined as frequency distribution data Hc (L).

A high saturation pixel ratio is calculated using the obtained frequency distribution data (step S43). The saturation is larger than a predetermined saturation threshold Thc, that is,
Hc (L)> Thc (Equation 8)
A high saturation pixel ratio Dc is obtained as a ratio of the number of pixels satisfying the above condition (the number of high saturation pixels) Nc and the total number of pixels mn. That is,
Dc = Nc / mn (Equation 9)
It is.

In the specific pixel ratio calculation process, first, the number of specific color pixels is counted, and the specific color pixel ratio is obtained as a ratio to the total number of pixels mn. For example, if the specific color pixel number Nr is used as the specific color pixel ratio Dr for the R color component,
Dr = Nr / mn (Equation 10)
It is. If the specific color pixel number is, for example, the specific color pixel number Nr for the R color component, the gradation value is larger than a predetermined gradation value threshold T (R), that is,
R (i, j)> T (R) (Equation 11)
Alternatively, the gradation value is smaller than the gradation value threshold T (R), that is,
R (i, j) <T (R) (Equation 12)
Alternatively, the chromaticity is smaller than the gradation value between the gradation value threshold values T1 (R) and T2 (R), that is,
T1 (R) <R (i, j) <T2 (R) (Equation 13)
This is the number of pixels that satisfy. Expressions 10 to 12 are appropriately selected according to the range of gradation values to be extracted. As described above, not only the specific color pixel ratio is calculated with the threshold condition for one color component, but also the number of pixels that satisfy all the threshold conditions for the R, G, and B color components, The aspect which calculates a ratio with the number of pixels may be sufficient.

FIG. 14 is a diagram for explaining the flow of the image line segment amount calculation process. In the image line segment calculation processing, first, the line segment amount in each of the vertical, horizontal, and diagonal (upward to the right and left upward) directions is obtained. Then, for each color component, a filter calculation process (vertical line segment filter process) for applying the 3 × 3 vertical line segment extraction filter FT2 shown in FIG. S51). For example, the gradation value R1 (i, j) after vertical line segment filtering of the R color component in the pixel (i, j) is
R1 (i, j) = 2 * {R (i, j) + R (i-1, j) + R (i + 1, j)}
-R (i-1, j-1) -R (i-1, j) -R (i-1, j + 1)
-R (i + 1, j-1) -R (i + 1, j) -R (i + 1, j + 1) (Equation 14)
Will be required.

Next, the gradation value R1 (i, j) obtained by the vertical line segment filtering process is averaged for all pixels (step S52), and this is summed for each color component to obtain the vertical line segment amount. Vertical line segment L1av is
L1av = {(ΣR1 (i, j)) + (ΣG1 (i, j)) + (ΣB1 (i, j))} / mn
.... (Formula 15)
Is required. With respect to the horizontal line segment, the right upward line segment, and the left upward line segment, the horizontal line segment extraction filter FT3, the left upward line segment extraction filter FT4, and the right upward line shown in FIGS. 16B, 16C, and 16D, respectively. By applying the minute extraction filter FT5, the filter calculation process is similarly performed, and the horizontal line segment amount L2av, the left-up line segment amount L3av, and the right-up line segment amount L4av are respectively calculated.

Then, by further summing the respective line segment amounts, an image line segment amount can be obtained. That is, the image line segment amount Lpav is
Lpav = Lpav + L2av + L3av + L4av (Equation 16)
It is obtained by calculation.

  By calculating the various image feature amounts as described above in each part of the image feature amount calculation unit 52, the image feature amount data DPC which is a data set including these components is obtained. That is, the image feature data DPC for the k-th image data among certain image data DP incorporated in the layout data DL is described as (k, Np, Lav, Dc, Dr, Lpav). Note that the above-described image feature amount calculation method and description format are merely examples, and may be implemented in other forms.

  When the image feature amount is calculated, the image classification processing is performed on all the image data DP incorporated in the layout data DL by the operation of the image classification processing unit 53 (step S4). When the image classification processing is performed and the image classification data DC is generated, the RGB images included in the printing document belong to the image category list field F5 of the work window W according to the contents thereof, as shown in FIG. All image categories are listed. Also in the image category display row F22 of the recipe application field F2, the image category into which the individual RGB images PC1 to PC4 are classified is displayed while contrasting with the thumbnail images PS1 to PS4. In FIG. 2, the thumbnail images PS1 to PS4 are displayed in units of pages. Alternatively, the thumbnail images PS1 to PS4 may be displayed in units of image categories based on the image classification data DC. At this time, a mode may be adopted in which a specific image category is selected in the image category list field F5 and only thumbnail images of RGB images included in the image category are displayed. In addition, a thumbnail image of CMYK images incorporated in the layout data DL may be displayed together in the recipe application field F2. In FIG. 2, it is assumed that the thumbnail image PS5 is a CMYK image.

  Once image classification processing has been performed, next, recipe information association processing by the operator is performed (step S5). The operation by the operator is executed on the work window W shown in FIG. As described above, when the recipe information association process is completed, all image categories to which the RGB images included in the printing document belong are listed and displayed in the image category list field F5. On the work window W, the operator selects a recipe icon IC of recipe information RI to be applied to a certain image category (that is, an image included in the image category) by using a mouse or the like (not shown) provided in the operation unit 33. Drag and drop (D & D) the image category in the list field F5. The recipe information application unit 62 receives the designation by the D & D and applies the image data corresponding to the RGB image belonging to the target image category out of the image data DP incorporated in the layout data DL and the D & D. Temporary data DT associated with the recipe information RI thus generated is generated. As a result, the same recipe information RI is applied to all images belonging to the same image category at the same time. Therefore, even when a large number of images are laid out, the processing is complicated. Is suppressed. The temporary data DT may be generated by directly adding the recipe information RI to the layout data DL in the same manner as the operation of the recipe information synthesizing unit 64 to be described later. The layout data DL and the recipe information RI are separately provided. It may be an aspect in which the association information between the two is given to either one.

  In addition, the temporary data DT is associated, and in the work window W, the recipe information applied to all the images belonging to the category is applied to the portion corresponding to the image in the setting recipe display column F23 of the recipe application field F2. Since the display showing the RI is made, the operator can visually recognize the association status. In FIG. 2, the recipe information RI named “landscape + machine” for the landscape category, the recipe information RI named “machine” for the “machine” category, and “recipe” category “ A state in which recipe information RI named “standard” is set is illustrated.

  Instead of applying the same recipe information RI for each image category as described above, the recipe information RI is applied to the RGB image displayed in the layout image display field F1 or the thumbnail image displayed in the recipe application field F2. The recipe information RI may be applied in units of images by D & D of the recipe icon IC.

  When the recipe information RI is applied to the image data DP, whether or not the application is appropriate is determined by simulation processing (step S6). In the case of FIG. 2, the operator selects one of the thumbnail images PS1 to PS4 that are displayed in the recipe application field F2 and to which the recipe information RI has already been applied. Correspondingly, the simulation processing unit 63 refers to the description of the image data DP corresponding to the selected thumbnail image and the description of the recipe information RI associated with the image data DP among the description contents of the temporary data DT. On the other hand, a simulation image PSM that simulates an image that is output when optimization processing according to the recipe information RI is performed in the output device 7 is displayed in the simulation image display field F6. Thereby, the operator can examine the validity of application of recipe information RI based on the simulation image PSM. FIG. 2 illustrates a case where simulation processing is performed on the RGB image PC2.

  As a result of checking the simulation image PSM, if the operator determines that the setting of the recipe information RI is not likely (NO in step S7), the operator inputs an instruction to click the recipe information fine adjustment button BT1 to associate the recipe information RI. Fine adjustment of the recipe information RI can be performed (step S8). When the recipe information fine adjustment button BT1 is clicked, for example, a predetermined fine adjustment menu item as shown in FIG. 8 is displayed in the fine adjustment setting field F7. When the operator performs an appropriate operation according to the displayed menu and executes instruction input, the recipe information RI parameters are adjusted by the action of the recipe information application unit, and the temporary data DT is rewritten by the corrected recipe information RI. . It is possible to perform the simulation process again by using the rewritten temporary data DT.

  When the operator determines that the recipe information RI to be applied to all the images is valid and clicks the OK button BT4, the applied recipe information RI is confirmed (step S9) (this is referred to as the confirmed recipe information RIF). Called). When the cancel button BT5 is clicked, all recipe information addition processing is canceled.

  The recipe information composition unit 64 receives the click of the OK button BT4 and performs a recipe information addition process (step S10). The recipe information addition process is realized by adding the confirmed recipe information RIF to the layout data DL. The layout data DL in which the recipe information RI is additionally written is referred to as recipe-added layout data DLR. In the layout data DLR with a recipe, preferably, a certain RGB image and the determined recipe information RIF corresponding to the RGB image are described as one set. At least the confirmed recipe information RIF corresponding to an image included in a certain page is described in the page.

  The layout data DLR with recipe obtained in this way is delivered to the output device 7. In the output device 7, by the operation of each unit constituting the image conversion processing unit 81 implemented in the control unit 71, first, prior to the output processing, first, optimization processing for printing based on the description content of the confirmed recipe information RIF, In other words, color space conversion processing and image correction processing that are optimal for each expression content are performed for each image. In addition, RIP processing by the RIP processing unit 82 and further printing processing are executed.

  As described above, when the printing document is layout data including RGB images, the output device performs processing (optimization processing) for optimizing the RGB image data included in the layout data for printing. Necessary recipe information (details will be described later) can be added to the layout data while being associated with each image data based on the image characteristics of the image data. As a result, even when information for performing optimization processing on RGB image data is not given in advance when creating layout data, the output device outputs a high-quality printed matter with excellent color reproducibility and the like. be able to.

  In addition, since a plurality of RGB images that are laid out can be classified according to the commonality of features represented by the RGB images, the same recipe information can be simultaneously applied to RGB images that belong to the same image category. Even when a large number of images are laid out, the complexity of processing is suppressed. In addition, it is possible to simulate the validity of the optimization process based on the recipe information once applied, and fine adjustment of the recipe information is possible. It can be used for optimization processing.

  The image processing information association processing apparatus according to the present embodiment may be provided in an output process responsible for output processing, and may check the submitted layout data prior to output processing. Alternatively, a layout may be used. In the design production process for creating data, an image processing information association processing device is used at the time of submission of layout data so that the manuscript can be submitted to the output process after appropriate image processing. May be. In the latter case, a kind of preflight check is also performed on the layout data. In the former case, for example, the output device may be integrated with the output device, and in the latter case, for example, the output device may be integrated with the layout data creation device. .

1 is a diagram illustrating a configuration of a printing system 1 according to an embodiment of the present invention. It is a figure which shows typically the work window W displayed on the display part. FIG. 6 is a diagram for explaining functions realized in a control unit 31. It is a figure explaining the flow of the data in the addition process of recipe information RI. It is a figure which shows typically the structure of recipe information RI. It is a figure which shows the setting item table TBL1 which is a list of the setting items in the characteristic setting parameter RI2. It is the figure which illustrated the correspondence of the image category and the content of the correction process which should be performed with respect to the image which belongs to the said image category as correction content table TBL2. It is a figure which shows fine adjustment menu table TBL3 which is a list of the typical process menu for setting fine adjustment parameter RI3. It is a figure for demonstrating the function implement | achieved in the control part. It is a figure for demonstrating the flow of the addition process of the recipe information performed with respect to layout data DL. It is a figure explaining the flow of processing in the steep peak number counting part 52a. It is a figure explaining the flow of a process in the edge component amount calculation part 52b. It is a figure explaining the flow of a process in the high saturation pixel ratio calculation part 42c. It is a figure explaining the flow of a process in the image segment amount calculation part 52e. It is a figure which shows Laplacian filter FT1. It is a figure which shows an arithmetic processing filter.

Explanation of symbols

DESCRIPTION OF SYMBOLS 1 Printing system 2 Layout data creation apparatus 3 Image processing information correlation processing apparatus 4 Calibration apparatus 6 Image scanner 7 Output apparatus F1 Layout image display column F2 Recipe application column F21 Image display column F22 Image category display column F23 Setting recipe display column F3 Recipe information List field F4 Recipe information detail display field F5 Image category list field F6 Simulation image display field F7 Fine adjustment setting field FT1 Laplacian filter FT2 Vertical line extraction filter FT3 Horizontal line extraction filter FT4 Left upward line extraction filter FT5 Right upward line extraction Filter IC Recipe icon P1C to PC4 RGB image PL1 Page layout image PS1 to PS4 Thumbnail image PSM Simulation image RI Recipe information TBL1 Setting item table TBL2 Positive content table TBL3 fine-tune menu table W task pane

Claims (22)

When an output process based on layout data described in a page description language and including at least one RGB image data is performed in a predetermined output device, each of the at least one RGB image data is performed prior to the output process. An apparatus for performing processing for associating image processing information necessary for image processing to be performed to obtain image data that can be output with each of the RGB image data,
An image processing information database in which a plurality of pieces of image processing information having different processing contents are registered;
Layout data reading means for reading the layout data;
Image data extraction means for extracting the at least one RGB image data from the layout data by searching description contents of the layout data regarding the RGB image data;
Image feature amount calculating means for calculating at least one image feature amount for each of the extracted at least one RGB image data;
Image classification means for collating the at least one image feature quantity with a predetermined classification criterion and classifying the at least one RGB image data into an image category determined in advance according to the classification criterion;
Association processing means for associating each of the at least one RGB image data with a designated one of the plurality of image processing information registered in the image processing information database;
Synthesizing means for synthesizing the image processing information to be associated with the layout data;
An image processing information association processing device comprising: The image processing information association processing apparatus according to claim 1,
The image processing information association processing apparatus, wherein the image processing includes color space conversion processing and image correction processing. The image processing information association processing device according to claim 1 or 2,
The associating means can simultaneously associate the same image processing information with RGB image data classified into the same image category among the at least one RGB image data. Information association processing device. An image processing information association processing apparatus according to any one of claims 1 to 3,
Simulation processing means capable of simulating image processing performed on the RGB image data in the predetermined output device based on the associated image processing information;
An image processing information association processing device further comprising: The image processing information association processing device according to claim 4,
Fine adjustment means for finely adjusting the associated image processing information in response to an instruction input based on the result of the simulation;
An image processing information association processing device further comprising: An image processing information association processing apparatus according to any one of claims 1 to 5,
The image feature amount calculating means includes
Steep peak counting means for counting the number of steep peaks, which is an image feature amount indicating the number of peaks that are sharper than a predetermined level appearing in the gradation value histogram in each color component of the RGB image represented by the RGB image data;
An image processing information association processing device comprising: An image processing information association processing apparatus according to any one of claims 1 to 5,
The image feature amount calculating means includes
An edge component amount calculating means for calculating an edge component amount that is an image feature amount indicating an abundance ratio of edges with respect to the entire image of each color component of the RGB image represented by the RGB image data;
An image processing information association processing device comprising: An image processing information association processing apparatus according to any one of claims 1 to 5,
The image feature amount calculating means includes
A high saturation pixel ratio calculating means for calculating a high saturation pixel ratio that is an image feature amount indicating a ratio of pixels having a saturation higher than a predetermined threshold for the RGB image represented by the RGB image data;
An image processing information association processing device comprising: An image processing information association processing apparatus according to any one of claims 1 to 5,
The image feature amount calculating means includes
A specific pixel ratio calculation means for calculating a specific color pixel ratio that is an image feature amount indicating a ratio of pixels belonging to a specific range in a gradation value among the RGB images represented by the RGB image data;
An image processing information association processing device comprising: An image processing information association processing apparatus according to any one of claims 1 to 5,
The image feature amount calculating means includes
Image line segment amount calculating means for calculating an image line segment amount that is an image feature amount indicating the number of line segments represented in the RGB image represented by the RGB image data;
An image processing information association processing device comprising: a) an output device,
When performing output processing based on layout data described in a page description language and including at least one RGB image data, the output processing can be performed for each of the at least one RGB image data prior to the output processing. Image processing means for performing image processing to obtain stable image data,
An output device comprising:
b) an image processing information associating device,
b-1) an image processing information database in which image processing information which is information enabling image processing to be performed in the output device and image processing information having different processing contents is registered;
b-2) layout data reading means for reading second layout data including at least one RGB image data;
b-3) Image data extraction means for extracting the at least one RGB image data by searching description contents of the layout data regarding the RGB image data;
Image feature amount calculating means for calculating at least one image feature amount for each of the extracted at least one RGB image data;
b-4) Image classification means for collating the at least one image feature quantity with a predetermined classification standard and classifying the at least one RGB image data into a predetermined image category according to the classification standard according to the result. ,
b-5) association processing means for associating each of the at least one RGB image data with a designated one of the plurality of image processing information registered in the image processing information database;
b-6) combining means for combining the image processing information to be associated with the second layout data;
An image processing information associating device comprising:
A printing system comprising: A method that enables output processing based on layout data that is described in a page description language and includes at least one RGB image data in a predetermined output device,
An image data extraction step of extracting the at least one RGB image data from the layout data by searching the description content of the layout data regarding the RGB image data;
An image feature amount calculating step for calculating at least one image feature amount for each of the extracted at least one RGB image data;
An image classification step of collating the at least one image feature amount with a predetermined classification criterion and classifying the at least one RGB image data into a predetermined image category according to the classification criterion according to the result,
Each of the at least one RGB image data and a plurality of pieces of image processing information registered in a predetermined image processing information database for performing image processing using the RGB image data as image data that can be output. An association process for associating with the specified information;
A synthesizing step for synthesizing the image processing information to be associated with the layout data;
A storage step of storing the combined layout data so as to be sent to the output device;
A method for enabling output processing of layout data. The output processing enabling method according to claim 12, comprising:
A layout data output processing enabling method, wherein the image processing includes color space conversion processing and image correction processing. The output processing enabling method according to claim 12 or 13,
The associating step can simultaneously associate the same image processing information with RGB image data classified into the same image category among the at least one RGB image data. To enable output processing. The output processing enabling method according to any one of claims 12 to 14,
A simulation processing step capable of simulating image processing performed on the RGB image data in the predetermined output device based on the associated image processing information;
The layout data output processing enabling method further comprising: The output processing enabling method according to claim 15,
A fine adjustment step of finely adjusting the associated image processing information in response to an instruction input based on the result of the simulation;
The layout data output processing enabling method further comprising: The output processing enabling method according to any one of claims 12 to 16,
The image feature amount calculating step includes:
A steep peak counting step for counting the number of steep peaks, which is an image feature amount indicating the number of peaks that are sharper than a predetermined level appearing in a gradation value histogram in each color component of the RGB image represented by the RGB image data;
A method for enabling output processing of layout data. The output processing enabling method according to any one of claims 12 to 16,
The image feature amount calculating means includes
An edge component amount calculating means for calculating an edge component amount that is an image feature amount indicating an abundance ratio of edges with respect to the entire image of each color component of the RGB image represented by the RGB image data;
A method for enabling output processing of layout data. The output processing enabling method according to any one of claims 12 to 16,
The image feature amount calculating means includes
A high saturation pixel ratio calculating means for calculating a high saturation pixel ratio that is an image feature amount indicating a ratio of pixels having a saturation higher than a predetermined threshold for the RGB image represented by the RGB image data;
A method for enabling output processing of layout data. The output processing enabling method according to any one of claims 12 to 16,
The image feature amount calculating means includes
A specific pixel ratio calculation means for calculating a specific color pixel ratio that is an image feature amount indicating a ratio of pixels belonging to a specific range in a gradation value among the RGB images represented by the RGB image data;
A method for enabling output processing of layout data. The output processing enabling method according to any one of claims 12 to 16,
The image feature amount calculating means includes
Image line segment amount calculating means for calculating an image line segment amount that is an image feature amount indicating the number of line segments represented in the RGB image represented by the RGB image data;
A method for enabling output processing of layout data. A program that causes a computer to function as the image processing information association processing apparatus according to any one of claims 1 to 10 by using a CPU and a memory included in the computer.

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